In clinical applications of structural magnetic resonance imaging (MRI), there exists a multi-objective trade-off between image quality, imaging time, and reconstruction time. Reducing imaging time for a given protocol is clearly beneficial from a cost prospective, and can also facilitate more detailed studies with the same patient throughput. Image quality tends to be a firm barrier placed by radiologists or researchers based upon requirements for data analysis. Finally, stringent hardware limitations exist for clinical FDA approved scanners. It is important to note that advances in MRI sequences and hardware continue to increase the computational burden for image reconstruction, e.g. large coil arrays, increased resolution, and multi-contrast studies. In this work, we investigate a highly scalable inverse algorithm intended to ameliorate the computational challenges associated with accurate compressed sensing (CS) reconstruction.
Sparse signal reconstruction has been introduced for MRI as a method to improve imaging time through random under-sampling of k-space. By assuming a sparsity inducing L1 image prior, the reconstruction problem can be formulated as an unconstrained optimization problem. This problem incorporates fidelity against the observed k-space samples with a penalty imposed on the sparsity prior. These methods have been shown to provide good image accuracy, but can significantly increase the computational burden for image reconstruction. This is especially evident with the inclusion of parallel imaging techniques (e.g., SENSE, GRAPPA, L1-Spirit). Several attempts have been made to reduce the computational requirements associated with sparse signal reconstruction. These iterative techniques rely on repetitive evaluation of forward CS parallel imaging models, however, and thus still have a computational burden.